Recognizing facial expressions at low resolutions in surveillance video typically involves manual processes that may or may not be aided or assisted by computer analytic algorithms. In both “Facial expression recognition based on Local Binary Patterns: A comprehensive study”, by Shan et al. and “Recognizing facial expression: machine learning and application to spontaneous behavior” by Bartlett et al., work has been done that classifies facial expressions at low resolutions, however only after the face and eyes have been manually selected. This process is typically time-consuming and requires the prolonged attention of several surveillance personnel; hence completely automating this approach is highly desirable.